Agentic AI in Capital Markets: Insights from SIFMA Ops 2025
In this blog
Operations: The Beating Heart of Capital Markets
Operations groups within capital market firms are the backbone of trade lifecycle management. They ensure every trade is confirmed, settled, reconciled and reported accurately and on time. From clearing to custody, they turn market intent into market reality. At the 2025 SIFMA Operations Conference, operations professionals emphasized their evolving roles with a focus on certain key areas:
Operational Resiliency: With increasing volatility and complexity in the markets (e.g., T+1 settlement, Treasury clearing mandates), operations teams are critical to maintaining market stability. There's an industry-wide push to collaborate on resiliency relative to third-party and fourth-party counterparts. Automation is seen as a key enabler to support near real-time trade flows and rapid recovery from disconnections, with agentic AI solutions playing an important role.
Technology and Data Integration: Operations teams are driving modernization through AI, data infrastructure and automation. Firms are embedding AI into broader corporate strategies that not only improve efficiency but also improve employee satisfaction, which leads to higher performance and accuracy. Creating, organizing and providing access to clean, structured data is essential for enabling speed, insight and proactive client service.
Digital Assets and Innovation: Operations groups are focused on digital assets and blockchain as industry adoption is accelerating, reflecting a broader shift from experimentation to implementation.
Adapting to Market Structure Changes: Operations groups play a central role in implementing extended trading hours, central clearing for Treasuries, new product types like ETFs and short-dated options and the shift from batch processing to Straight Through Processing (STP).
Overall, the conference highlighted operations as a strategic function enabling market transformation through resilient systems, integrated technology and agile response to regulatory and structural shifts.
AI: Natural Use Cases in Cap Market Operations
SIFMA's poll showed over 65% of respondents view AI/automation as the most significant transformation expected in ops over the next 3–5 years. This is no surprise, as operations is a discipline built on repeatable processes, complex exception handling and high-volume communications—all of which map cleanly to LLM capabilities. At WWT, we predict that as GenAI agents mature and become more deeply embedded in real-time workflows, operations will move from being process executors to being process designers and overseers, freeing up talent for more analytical and strategic functions.
Large Language Models (LLMs) and other model types are a natural fit for many operational workflows in capital markets, given the data-heavy, rule-driven and natural-language document-focused nature of the work. This was strongly emphasized at the 2025 SIFMA Ops Conference, where it became evident that operations professionals are turning to AI not just for insights and predictive analytics, but also for action — embedding intelligence into workflows to minimize manual intervention. Commenters also mentioned that AI has improved job satisfaction among operations staff by freeing up time for professional development and allowing them to focus on higher-level tasks, rather than repetitive work like data entry and document processing.
Here are some of the key areas where generative AI is found in operational workflows:
Document Processing & Interpretation
One example in the SIFMA Ops 2025 debrief document is the digitization of loan documents to streamline back-office processing. GenAI excels at parsing semi-structured or unstructured documents (e.g., PDFs, scanned agreements) by extracting key fields and standardizing them for use in downstream systems. The capability is especially useful in high-volume operations such as processing securities lending, repo, onboarding, corporate action and various regulatory documentation.
Exception Management & Reconciliation
LLMs can review large volumes of settlement, payment or trade matching exceptions and automatically categorize them by root cause. In advanced scenarios, LLMs can be put to work drafting proposed remediation steps or escalation memos. This is particularly impactful in T+1 environments where time windows are compressed and can be critically important when falling back to more manual processes during market incidents.
Workflow Assistants & Agentic AI
As mentioned in the SIFMA Ops 2025 debrief document, there has been a shift toward agentic AI solutions that can take action. AI agents can monitor trade settlement queues and automatically initiate follow-up requests with custodians, counterparties or internal ops teams via chat or email — not just via templates, but in a rich human-like manner — dramatically reducing response times and operational drag.
Client & Internal Communications
Operations often need to send templated but personalized messages — e.g., fail notifications, KYC requests or corporate action updates. LLMs can generate these messages with contextual nuance, drawing from trade data, prior history or regulatory context, while staying compliant with tone and terminology standards. LLMs also have a role in the surveillance of client communications to ensure regulatory compliance and reporting.
Playbook Automation & Training
Playbooks and SOPs are the backbone of operational continuity. LLMs can turn these into interactive assistants, answering "how do I handle X?" questions in real time. For example, a junior staff member could ask, "What's the protocol if a Treasury repo trade fails to clear by cutoff?" and get a tailored, policy-compliant response drawn from firm-specific documentation.
KYC/AML in Digital Asset Contexts
Firms are exploring embedding KYC/AML logic directly into blockchain smart contracts. LLMs can assist by reviewing smart contract code or transaction flows to identify compliance risks or explain how embedded controls are operating, bridging ops, compliance and tech.
No-Code & Low-Code Tools
AI coding assistants can help create (sometimes complex) workflows using traditional tools such as Apache Airflow, pure Python and/or other scripting languages. They are even available within business applications such as Excel. Newer low-code agentic workflow tools such as n8nare also available.
Advanced Computing in Operations
In capital markets operations, predictive AI, statistical analytics and other compute-heavy solutions are becoming essential for moving from reactive to proactive workflows. These use cases extend beyond automation, focusing instead on forecasting, optimization and reducing systemic risk. They often require more advanced models and computing infrastructure than traditional LLMs. To support these workloads, firms may need secure, high-throughput environments—such as private clouds, GPU clusters, or hybrid HPC systems—with strict auditability and data governance, especially to comply with regulations like SEC Reg SCI, FINRA CAT or EMIR/DORA in the EU.
Settlement Failure Prediction - Use machine learning models trained on historical trade, counterparty and market data to forecast likely settlement fails before they occur—prioritizing intervention in T+1 windows.
Operational Risk Forecasting - Predict outages, reconciliation breaks or SLA violations across post-trade systems using system telemetry, support tickets and incident history.
Cash & Collateral Forecasting - Anticipate funding needs or collateral shortfalls by modeling historical patterns, margin calls and market volatility.
Exception Trend Analysis - Identify recurring root causes in reconciliations, breaks or processing errors across products, venues or clients by using cluster analysis or regression techniques.
Process Bottleneck Identification - Apply queueing models or time-series analytics to workflow telemetry (e.g., trade matching times, custodial confirmations) to pinpoint where latency or throughput degrades.
Client Behavior Profiling - Analyze trade flow and instruction behavior to surface habitual late submitters or high-risk counterparties, informing operational SLAs or risk buffers.
Real-time Risk Aggregation - Intraday aggregation of credit, market and operational risk metrics across desks and entities—enabled by GPU-accelerated compute or distributed clusters.
Simulation & Stress Testing - Monte Carlo simulations of settlement chains, liquidity shocks or cyber events to stress operational processes under extreme scenarios.
Smart Routing & Optimization - Use reinforcement learning or optimization solvers to dynamically route trades, allocate collateral or schedule batch jobs based on constraints and goals.
WWT Can Help
With deep expertise in secure infrastructure, AI integration and data architecture, WWT helps firms deploy solutions like workflow automation, predictive analytics and GenAI at scale—whether on-prem, in the cloud or hybrid. From accelerating T+1 readiness to enabling real-time risk insights and AI-augmented trade ops, WWT empowers operations teams to move faster, reduce risk and stay compliant in a transforming regulatory and market landscape. WWT can not only supply the necessary compute, storage and networking infrastructure but can also assist in the specification, planning, development, deployment and subsequent support of complex systems that leverage those hardware resources via advanced AIs and traditional software components.